Systematic review of reverse vaccinology and immunoinformatics data for non-viral sexually transmitted infections

Abstract Sexually Transmitted Infections (STIs) are a public health burden rising in developed and developing nations. The World Health Organization estimates nearly 374 million new cases of curable STIs yearly. Global efforts to control their spread have been insufficient in fulfilling their objective. As there is no vaccine for many of these infections, these efforts are focused on education and condom distribution. The development of vaccines for STIs is vital for successfully halting their spread. The field of immunoinformatics is a powerful new tool for vaccine development, allowing for the identification of vaccine candidates within a bacterium’s genome and allowing for the design of new genome-based vaccine peptides. The goal of this review was to evaluate the usage of immunoinformatics in research focused on non-viral STIs, identifying fields where research efforts are concentrated. Here we describe gaps in applying these techniques, as in the case of Treponema pallidum and Trichomonas vaginalis

Molecular epidemiology of sisal bole rot disease suggests a potential phytosanitary crisis in Brazilian production areas

Sisal bole rot disease is the major phytosanitary problem of Agave plantations in Brazil. The disease is caused by a cryptic species of Aspergillus: A. welwitschiae. To date, the only way to diagnose the disease was to observe external symptoms, visible only when the plant is already compromised, or through the isolation and sequencing of the pathogen, which requires cutting the entire plant for bole tissue sampling. We developed a new primer set based on a unique gene region of A. welwitschiae, which can detect the phytopathogenic strains through PCR directly from sisal leaves. Using the new marker to study the main sisal-producing areas in Brazil, we discovered a troublesome situation. The main producing areas of this crop had a pathogen incidence of 78%–88%. The dispersion index indicates a regular spatial pattern for disease distribution, suggesting that the use of contaminated suckers to establish new fields may be the main disease-spreading mechanism. Altogether, the high incidence of the pathogen, the unavailability of clean plants, the unpredictability of disease progression, and the low investment capacity of farmers reveal the vulnerability of this sector to a potential phytosanitary crisis. By correlating the disease symptomatology with soil nutritional traits, we suggest that higher potassium availability might decrease visual symptoms, while phosphorus may have the opposite effect. Also, we observe a potential cultivar effect, suggesting that common sisal may be more susceptible than hybrid cultivars (especially H400). This new molecular tool is a significant advance for understanding the disease, enabling the implementation of a monitoring program and studies that may lead to pathogen control strategies and changes in the Brazilian production model

Detecting Network Communities: An Application to Phylogenetic Analysis

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis

PLoS Computational Biology

p. 13This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix Mˆ . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (.70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis

First report and genetic characterization of the highly pathogenic avian influenza A(H5N1) virus in Cabot's tern (Thalasseus acuflavidus), Brazil

In 2021, the H5N1 virus lineage 2.3.4.4b spread to the Americas, causing high mortality in wild and domestic avian populations. South American countries along the Pacific migratory route have reported wild bird deaths due to A/H5Nx virus since October 2022. However, limited genomic data resulted in no cases reported in Brazil until May 2023.Brazil reported its first case of highly pathogenic avian influenza virus (HPAI A/H5N1) in May 2023. The virus was detected in Cabot's tern specimen in Marataízes, Espírito Santo. Cases were also found in backyard poultry and other wild birds, but no human or commercial poultry cases occurred. HPAI poses risks to the poultry industry, food security, and public health.Researchers used next-gen sequencing and phylogenetic analysis to study the Brazilian sample. It confirmed its affiliation with the 2.3.4.4b clade and proximity to sequences from Chile and Peru.This sheds light on the spread and evolution of HPAI A/H5N1 in the Americas, emphasizing continuous monitoring to mitigate risks for both avian and human populations. Understanding the virus's genetics and transmission allows implementing effective control measures to protect public health and the poultry industry